Rice Genomics and Genetics 2024, Vol.15, No.2, 48-57 http://cropscipublisher.com/index.php/rgg 53 5 Application of GWAS in Rice Genetic Breeding 5.1 Methods and strategies for genetic improvement of rice using GWAS GWAS is a highly efficient strategy that relies on the analysis of genomic information and phenotypic data from large genetic resources to identify genes or genetic markers associated with important agronomic traits. This method can not only reveal the genetic basis of complex traits, but also provide accurate molecular targets for the improvement of rice varieties. Collect rice germplasm resources with a wide range of genetic backgrounds, including wild species, landraces and improved varieties. The genetic diversity of these germplasm resources is the basis of GWAS analysis and can improve the coverage and accuracy of association analysis. Conduct detailed phenotypic evaluation of the collected germplasm resources, including yield, disease resistance, stress tolerance, grain quality and other agronomic traits. The accuracy and reproducibility of phenotypic data are crucial for subsequent GWAS analysis. Use high-throughput sequencing technology to sequence the entire genome of germplasm resources to obtain high-density genotype data. This includes genetic variation information such as single nucleotide polymorphisms (SNPs) and insertions and deletions (InDels). Perform quality control on the obtained genotype data, including removing low-quality sequences, correcting sequencing errors, filling in missing genotype data, etc., to ensure the accuracy of subsequent analysis. Use statistical methods to analyze the association between genotypic and phenotypic data and identify genetic markers or gene regions that are significantly associated with specific agronomic traits. This step may involve a variety of statistical models to control for the effects of population structure and genetic background. Based on the GWAS analysis results, combined with gene annotation information and bioinformatics tools, candidate genes associated with traits are screened out. Further use methods such as gene expression analysis and functional verification experiments to determine the key regulatory genes of traits. For example, after GWAS obtains candidate functional genes, the function of the genes can also be verified through transgene overexpression. For example, in the association analysis of rice, the author found that the gene LOC_Os01g62780 affects the heading time of rice, so the gene sequence containing haplotypes A and B was introduced into Nipponbare containing haplotype A (Figure 2) (Yano et al., 2016). The results showed that the phenotype of plants transferred to haplotype A did not change, while the heading stage of plants transferred to haplotype B became later. This proves that the LOC_Os01g62780 gene is a gene that controls rice heading time (Yano et al., 2016). Figure 2 Effect of LOC_Os01g62780gene on rice heading time ((Adopted from Yano et al., 2016) Based on the key genes or genetic markers identified by GWAS, develop efficient molecular markers, such as SNP markers, simple sequence repeat (SSR) markers, etc. In the process of rice breeding, these molecular markers are used for efficient genetic background screening and rapid selection of target traits, significantly improving breeding efficiency and accuracy. For important genes identified by GWAS, gene editing technologies such as CRISPR/Cas9 can be used to
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